nag_dgerfs (f07ahc) (PDF version)
f07 Chapter Contents
f07 Chapter Introduction
NAG Library Manual

NAG Library Function Document

nag_dgerfs (f07ahc)

 Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_dgerfs (f07ahc) returns error bounds for the solution of a real system of linear equations with multiple right-hand sides, AX=B or ATX=B. It improves the solution by iterative refinement, in order to reduce the backward error as much as possible.

2  Specification

#include <nag.h>
#include <nagf07.h>
void  nag_dgerfs (Nag_OrderType order, Nag_TransType trans, Integer n, Integer nrhs, const double a[], Integer pda, const double af[], Integer pdaf, const Integer ipiv[], const double b[], Integer pdb, double x[], Integer pdx, double ferr[], double berr[], NagError *fail)

3  Description

nag_dgerfs (f07ahc) returns the backward errors and estimated bounds on the forward errors for the solution of a real system of linear equations with multiple right-hand sides AX=B or ATX=B. The function handles each right-hand side vector (stored as a column of the matrix B) independently, so we describe the function of nag_dgerfs (f07ahc) in terms of a single right-hand side b and solution x.
Given a computed solution x, the function computes the component-wise backward error β. This is the size of the smallest relative perturbation in each element of A and b such that x is the exact solution of a perturbed system
A+δAx=b+δb δaijβaij   and   δbiβbi .  
Then the function estimates a bound for the component-wise forward error in the computed solution, defined by:
maxi xi - x^i / maxi xi  
where x^ is the true solution.
For details of the method, see the f07 Chapter Introduction.

4  References

Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

5  Arguments

1:     order Nag_OrderTypeInput
On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by order=Nag_RowMajor. See Section 3.2.1.3 in the Essential Introduction for a more detailed explanation of the use of this argument.
Constraint: order=Nag_RowMajor or Nag_ColMajor.
2:     trans Nag_TransTypeInput
On entry: indicates the form of the linear equations for which X is the computed solution.
trans=Nag_NoTrans
The linear equations are of the form AX=B.
trans=Nag_Trans or Nag_ConjTrans
The linear equations are of the form ATX=B.
Constraint: trans=Nag_NoTrans, Nag_Trans or Nag_ConjTrans.
3:     n IntegerInput
On entry: n, the order of the matrix A.
Constraint: n0.
4:     nrhs IntegerInput
On entry: r, the number of right-hand sides.
Constraint: nrhs0.
5:     a[dim] const doubleInput
Note: the dimension, dim, of the array a must be at least max1,pda×n.
The i,jth element of the matrix A is stored in
  • a[j-1×pda+i-1] when order=Nag_ColMajor;
  • a[i-1×pda+j-1] when order=Nag_RowMajor.
On entry: the n by n original matrix A as supplied to nag_dgetrf (f07adc).
6:     pda IntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array a.
Constraint: pdamax1,n.
7:     af[dim] const doubleInput
Note: the dimension, dim, of the array af must be at least max1,pdaf×n.
The i,jth element of the matrix is stored in
  • af[j-1×pdaf+i-1] when order=Nag_ColMajor;
  • af[i-1×pdaf+j-1] when order=Nag_RowMajor.
On entry: the LU factorization of A, as returned by nag_dgetrf (f07adc).
8:     pdaf IntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array af.
Constraint: pdafmax1,n.
9:     ipiv[dim] const IntegerInput
Note: the dimension, dim, of the array ipiv must be at least max1,n.
On entry: the pivot indices, as returned by nag_dgetrf (f07adc).
10:   b[dim] const doubleInput
Note: the dimension, dim, of the array b must be at least
  • max1,pdb×nrhs when order=Nag_ColMajor;
  • max1,n×pdb when order=Nag_RowMajor.
The i,jth element of the matrix B is stored in
  • b[j-1×pdb+i-1] when order=Nag_ColMajor;
  • b[i-1×pdb+j-1] when order=Nag_RowMajor.
On entry: the n by r right-hand side matrix B.
11:   pdb IntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array b.
Constraints:
  • if order=Nag_ColMajor, pdbmax1,n;
  • if order=Nag_RowMajor, pdbmax1,nrhs.
12:   x[dim] doubleInput/Output
Note: the dimension, dim, of the array x must be at least
  • max1,pdx×nrhs when order=Nag_ColMajor;
  • max1,n×pdx when order=Nag_RowMajor.
The i,jth element of the matrix X is stored in
  • x[j-1×pdx+i-1] when order=Nag_ColMajor;
  • x[i-1×pdx+j-1] when order=Nag_RowMajor.
On entry: the n by r solution matrix X, as returned by nag_dgetrs (f07aec).
On exit: the improved solution matrix X.
13:   pdx IntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array x.
Constraints:
  • if order=Nag_ColMajor, pdxmax1,n;
  • if order=Nag_RowMajor, pdxmax1,nrhs.
14:   ferr[nrhs] doubleOutput
On exit: ferr[j-1] contains an estimated error bound for the jth solution vector, that is, the jth column of X, for j=1,2,,r.
15:   berr[nrhs] doubleOutput
On exit: berr[j-1] contains the component-wise backward error bound β for the jth solution vector, that is, the jth column of X, for j=1,2,,r.
16:   fail NagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

6  Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.2.1.2 in the Essential Introduction for further information.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_INT
On entry, n=value.
Constraint: n0.
On entry, nrhs=value.
Constraint: nrhs0.
On entry, pda=value.
Constraint: pda>0.
On entry, pdaf=value.
Constraint: pdaf>0.
On entry, pdb=value.
Constraint: pdb>0.
On entry, pdx=value.
Constraint: pdx>0.
NE_INT_2
On entry, pda=value and n=value.
Constraint: pdamax1,n.
On entry, pdaf=value and n=value.
Constraint: pdafmax1,n.
On entry, pdb=value and n=value.
Constraint: pdbmax1,n.
On entry, pdb=value and nrhs=value.
Constraint: pdbmax1,nrhs.
On entry, pdx=value and n=value.
Constraint: pdxmax1,n.
On entry, pdx=value and nrhs=value.
Constraint: pdxmax1,nrhs.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
An unexpected error has been triggered by this function. Please contact NAG.
See Section 3.6.6 in the Essential Introduction for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 3.6.5 in the Essential Introduction for further information.

7  Accuracy

The bounds returned in ferr are not rigorous, because they are estimated, not computed exactly; but in practice they almost always overestimate the actual error.

8  Parallelism and Performance

nag_dgerfs (f07ahc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
nag_dgerfs (f07ahc) makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9  Further Comments

For each right-hand side, computation of the backward error involves a minimum of 4n2 floating-point operations. Each step of iterative refinement involves an additional 6n2 operations. At most five steps of iterative refinement are performed, but usually only one or two steps are required.
Estimating the forward error involves solving a number of systems of linear equations of the form Ax=b or ATx=b; the number is usually 4 or 5 and never more than 11. Each solution involves approximately 2n2 operations.
The complex analogue of this function is nag_zgerfs (f07avc).

10  Example

This example solves the system of equations AX=B using iterative refinement and to compute the forward and backward error bounds, where
A= 1.80 2.88 2.05 -0.89 5.25 -2.95 -0.95 -3.80 1.58 -2.69 -2.90 -1.04 -1.11 -0.66 -0.59 0.80   and   B= 9.52 18.47 24.35 2.25 0.77 -13.28 -6.22 -6.21 .  
Here A is nonsymmetric and must first be factorized by nag_dgetrf (f07adc).

10.1  Program Text

Program Text (f07ahce.c)

10.2  Program Data

Program Data (f07ahce.d)

10.3  Program Results

Program Results (f07ahce.r)


nag_dgerfs (f07ahc) (PDF version)
f07 Chapter Contents
f07 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2015